ilaykav

ilaykav / scvelo-rs

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Drop-in Rust + PyO3 acceleration of scvelo for single-cell RNA velocity

pypi.orgprojectscvelo-rs bioinformatics pyo3 rna-velocity rna-velocity-estimation scvelo
10
0
100% credibility
Found May 12, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

A high-performance replacement for scVelo that accelerates single-cell RNA velocity analysis while matching original results exactly.

How It Works

1
🔬 Discover a faster way to track cell changes

You're a biologist frustrated with slow analysis of single-cell data, and you find this tool that speeds up studying how cells evolve.

2
📥 Add it to your setup easily

Follow simple instructions to bring this speedy helper into your existing biology software world.

3
Pick your starting style
🔄
Fully switch over

Replace your old analysis tool with this faster one for everything.

✏️
Quick tweak

Just update a few lines in your current notebooks to unlock the speed.

4
📊 Load your cell data

Bring in your experiment's cell measurements, ready to explore.

5
⚡ Run and feel the speed

Hit go and watch your heavy analysis fly through in seconds or minutes, not hours.

📈 Unlock cell insights fast

Enjoy crisp maps of cell movements and behaviors, speeding up your discoveries.

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AI-Generated Review

What is scvelo-rs?

scvelo-rs is a drop-in Rust acceleration for scvelo, turbocharging single-cell RNA velocity analysis on unspliced/spliced counts. Pip-install the package, then swap `import scvelo as scv` for `import scvelo_rs as scv`, or add `scvelo_rs.patch()` to route bottlenecks like recover_dynamics and velocity_graph to Rust via PyO3. It delivers bit-exact outputs to the original Python, with 30-40x speedups and 2-4x less peak memory on 5k-100k cell atlases.

Why is it gaining traction?

Zero-code-change drop-in makes it a no-brainer upgrade—no rewriting pipelines or chasing NaN drift. CPU-only wheels run anywhere (Linux ARM, macOS, Windows, HPC), skipping CUDA/Numba warmup. Benchmarks show recover_dynamics flying from 43s to 1s on 5k cells, with parity notebooks proving 99.9% gene equivalence.

Who should use this?

Single-cell bioinformaticians crunching pancreas or dentate gyrus datasets in scvelo. Data scientists hitting memory walls on 50k+ cell velocity graphs. Labs needing reproducible, atlas-scale RNA velocity without GPU clusters.

Verdict

Grab it for instant acceleration if scvelo's your daily driver—patch mode keeps everything else intact. 10 stars and 1.0% credibility score flag early maturity (solid README/notebooks, but watch for v0.2 docs); test on your data first.

(198 words)

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